SCIEN Colloquium presents Deep Learning Meets Computational Imaging: Combining Data-Driven Priors and Domain Knowledge
Neural networks have surpassed the performance of virtually any traditional computer vision algorithm thanks to their ability to learn priors directly from the data. The common encoder/decoder with skip connections architecture, for instance, has been successfully employed in a number of tasks, from optical flow estimation, to image deblurring, image denoising, and even higher level tasks, such as image-to-image translation.
To improve the results further, one must leverage the constraints of the specific problem at hand, in particular when the domain is fairly well understood, such as the case of computational imaging.
In this talk I will describe a few of my recent projects that build on this observation, ranging from reflection removal, to novel view synthesis, and deblurring.